The quality of speech captured by personal computers can be degraded by environmental noise and/or by reverberation (e.g., caused by the sound waves reflecting off walls and other surfaces). Quasi-stationary noise produced by computer fans and air conditioning can be significantly reduced by spectral subtraction or similar techniques. In contrast, removing non-stationary noise and/or reducing the distortion caused by reverberation are much harder problems. De-reverberation is a difficult blind deconvolution problem due to the broadband nature of speech and the high order of the equivalent impulse response from the speaker's mouth to the microphone. The problem is, of course, alleviated by the use of microphone headsets, but those are usually inconvenient to the user.
Using signal processing to improve the quality of speech acquired by microphone(s) has been a long-standing interest in the Digital Signal Processing community, with some of the most promising technologies being based on microphone arrays. The microphone array literature is particularly populated with algorithms based on the Generalized Sidelobe Canceller (GSC), but performance degrades quickly with reverberation. Other algorithms are based on optimum filtering concepts, or signal subspace projection. A different approach comes from Blind Source Separation (BSS). Curiously, while BSS techniques perform extremely well in some environments, they tend to be overly sensitive to ambient conditions (e.g., room reverberation), and perform poorly in most real-world scenarios.